Machine learning research

META is a Natural Language learning project whose specific objective is to acquire new word definitions and new concepts from contextual information in interactive dialogues. It is an instance of a learning-from-examples method, with a difference: learning proceeds in a reactive, knowledge-rich environment. Our initial research indicates that the interactive nature of the environment ought to be a crucial component of any general learning system. In brief, our investigations have led us to formulate the following hypotheses, which we intend to test and pursue further in the immediate future:1. Learning requires progressive refinement -- It is unreasonable to expect that a computer system (or a human) learn a concept or a skill without error, in its full embellished form, in one brief learning session. It must undergo a sequence of progressive test-and-update stages. In other words, a concept can be learned by first inducing a rough approximation of its final form and successively correcting this approximation with more accurate, more detailed information.2. Interaction with a reactive environment -- Interaction is the engine that drives learning processes. A learner must be able to direct queries to its teacher or perform experiments on its environment. It must be able test out new concepts and skills as it learns them.3. Reasoning by Analogy -- The more a system can learn by relating new concepts to old, by modifying existing concepts, or using chunks of existing concepts as building blocks, the more robust and general its learning mechanisms will be.